Overview

Dataset statistics

Number of variables64
Number of observations9
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory526.2 B

Variable types

Categorical38
Numeric26

Alerts

Leistungserbringer has constant value "Leistungserbringer - Total"Constant
Finanzierungsregime has constant value "Finanzierungsregime - Total"Constant
1995 is highly overall correlated with 1996 and 60 other fieldsHigh correlation
1996 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
1997 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
1998 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
1999 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2000 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2001 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2002 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2003 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2004 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2005 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2006 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2007 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2008 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2009 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2010 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2011 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2012 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2013 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2014 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2015 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2016 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2017 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2018 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2019 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
2020 is highly overall correlated with 1995 and 60 other fieldsHigh correlation
Leistung is highly overall correlated with 1995 and 60 other fieldsHigh correlation
1960 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1961 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1962 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1963 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1964 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1965 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1966 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1967 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1968 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1969 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1970 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1971 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1972 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1973 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1974 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1975 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1976 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1977 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1978 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1979 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1980 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1981 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1982 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1983 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1984 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1985 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1986 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1987 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1988 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1989 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1990 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1991 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1992 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1993 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
1994 is highly overall correlated with 1995 and 26 other fieldsHigh correlation
Leistung is uniformly distributedUniform
Leistung has unique valuesUnique
1995 has unique valuesUnique
1996 has unique valuesUnique
1997 has unique valuesUnique
1998 has unique valuesUnique
1999 has unique valuesUnique
2000 has unique valuesUnique
2001 has unique valuesUnique
2002 has unique valuesUnique
2003 has unique valuesUnique
2004 has unique valuesUnique
2005 has unique valuesUnique
2006 has unique valuesUnique
2007 has unique valuesUnique
2008 has unique valuesUnique
2009 has unique valuesUnique
2010 has unique valuesUnique
2011 has unique valuesUnique
2012 has unique valuesUnique
2013 has unique valuesUnique
2014 has unique valuesUnique
2015 has unique valuesUnique
2016 has unique valuesUnique
2017 has unique valuesUnique
2018 has unique valuesUnique
2019 has unique valuesUnique
2020 has unique valuesUnique

Reproduction

Analysis started2022-12-28 10:59:17.318912
Analysis finished2022-12-28 10:59:44.960639
Duration27.64 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size200.0 B
Leistungserbringer - Total

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters234
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeistungserbringer - Total
2nd rowLeistungserbringer - Total
3rd rowLeistungserbringer - Total
4th rowLeistungserbringer - Total
5th rowLeistungserbringer - Total

Common Values

ValueCountFrequency (%)
Leistungserbringer - Total 9
100.0%

Length

2022-12-28T11:59:44.986402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.021831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
leistungserbringer 9
33.3%
9
33.3%
total 9
33.3%

Most occurring characters

ValueCountFrequency (%)
e 27
11.5%
r 27
11.5%
i 18
 
7.7%
s 18
 
7.7%
t 18
 
7.7%
n 18
 
7.7%
g 18
 
7.7%
18
 
7.7%
L 9
 
3.8%
u 9
 
3.8%
Other values (6) 54
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 189
80.8%
Space Separator 18
 
7.7%
Uppercase Letter 18
 
7.7%
Dash Punctuation 9
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27
14.3%
r 27
14.3%
i 18
9.5%
s 18
9.5%
t 18
9.5%
n 18
9.5%
g 18
9.5%
u 9
 
4.8%
b 9
 
4.8%
o 9
 
4.8%
Other values (2) 18
9.5%
Uppercase Letter
ValueCountFrequency (%)
L 9
50.0%
T 9
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 207
88.5%
Common 27
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27
13.0%
r 27
13.0%
i 18
8.7%
s 18
8.7%
t 18
8.7%
n 18
8.7%
g 18
8.7%
L 9
 
4.3%
u 9
 
4.3%
b 9
 
4.3%
Other values (4) 36
17.4%
Common
ValueCountFrequency (%)
18
66.7%
- 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27
11.5%
r 27
11.5%
i 18
 
7.7%
s 18
 
7.7%
t 18
 
7.7%
n 18
 
7.7%
g 18
 
7.7%
18
 
7.7%
L 9
 
3.8%
u 9
 
3.8%
Other values (6) 54
23.1%

Leistung
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size200.0 B
Leistung - Total
>> L Stationäre Kurativbehandlung
>> M Ambulante Kurativbehandlung
>> N Rehabilitation
>> O Langzeitpflege
Other values (4)

Length

Max length36
Median length32
Mean length22.888889
Min length15

Characters and Unicode

Total characters206
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st rowLeistung - Total
2nd row>> L Stationäre Kurativbehandlung
3rd row>> M Ambulante Kurativbehandlung
4th row>> N Rehabilitation
5th row>> O Langzeitpflege

Common Values

ValueCountFrequency (%)
Leistung - Total 1
11.1%
>> L Stationäre Kurativbehandlung 1
11.1%
>> M Ambulante Kurativbehandlung 1
11.1%
>> N Rehabilitation 1
11.1%
>> O Langzeitpflege 1
11.1%
>> P Unterstützende Dienstleistungen 1
11.1%
>> Q Gesundheitsgüter 1
11.1%
>> R Prävention 1
11.1%
>> S Verwaltung 1
11.1%

Length

2022-12-28T11:59:45.052764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.097032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
9
30.0%
kurativbehandlung 2
 
6.7%
leistung 1
 
3.3%
p 1
 
3.3%
s 1
 
3.3%
prävention 1
 
3.3%
r 1
 
3.3%
gesundheitsgüter 1
 
3.3%
q 1
 
3.3%
dienstleistungen 1
 
3.3%
Other values (11) 11
36.7%

Most occurring characters

ValueCountFrequency (%)
21
 
10.2%
e 20
 
9.7%
t 19
 
9.2%
n 18
 
8.7%
> 16
 
7.8%
i 12
 
5.8%
a 11
 
5.3%
u 9
 
4.4%
g 8
 
3.9%
l 8
 
3.9%
Other values (30) 64
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 147
71.4%
Space Separator 21
 
10.2%
Uppercase Letter 21
 
10.2%
Math Symbol 16
 
7.8%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20
13.6%
t 19
12.9%
n 18
12.2%
i 12
8.2%
a 11
 
7.5%
u 9
 
6.1%
g 8
 
5.4%
l 8
 
5.4%
r 7
 
4.8%
s 6
 
4.1%
Other values (12) 29
19.7%
Uppercase Letter
ValueCountFrequency (%)
L 3
14.3%
K 2
 
9.5%
R 2
 
9.5%
P 2
 
9.5%
S 2
 
9.5%
A 1
 
4.8%
N 1
 
4.8%
M 1
 
4.8%
O 1
 
4.8%
T 1
 
4.8%
Other values (5) 5
23.8%
Space Separator
ValueCountFrequency (%)
21
100.0%
Math Symbol
ValueCountFrequency (%)
> 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168
81.6%
Common 38
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20
 
11.9%
t 19
 
11.3%
n 18
 
10.7%
i 12
 
7.1%
a 11
 
6.5%
u 9
 
5.4%
g 8
 
4.8%
l 8
 
4.8%
r 7
 
4.2%
s 6
 
3.6%
Other values (27) 50
29.8%
Common
ValueCountFrequency (%)
21
55.3%
> 16
42.1%
- 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202
98.1%
None 4
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
 
10.4%
e 20
 
9.9%
t 19
 
9.4%
n 18
 
8.9%
> 16
 
7.9%
i 12
 
5.9%
a 11
 
5.4%
u 9
 
4.5%
g 8
 
4.0%
l 8
 
4.0%
Other values (28) 60
29.7%
None
ValueCountFrequency (%)
ü 2
50.0%
ä 2
50.0%
Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size200.0 B
Finanzierungsregime - Total

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters243
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinanzierungsregime - Total
2nd rowFinanzierungsregime - Total
3rd rowFinanzierungsregime - Total
4th rowFinanzierungsregime - Total
5th rowFinanzierungsregime - Total

Common Values

ValueCountFrequency (%)
Finanzierungsregime - Total 9
100.0%

Length

2022-12-28T11:59:45.144127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.177113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
finanzierungsregime 9
33.3%
9
33.3%
total 9
33.3%

Most occurring characters

ValueCountFrequency (%)
n 27
11.1%
e 27
11.1%
i 27
11.1%
g 18
 
7.4%
18
 
7.4%
a 18
 
7.4%
r 18
 
7.4%
t 9
 
3.7%
o 9
 
3.7%
T 9
 
3.7%
Other values (7) 63
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 198
81.5%
Space Separator 18
 
7.4%
Uppercase Letter 18
 
7.4%
Dash Punctuation 9
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 27
13.6%
e 27
13.6%
i 27
13.6%
g 18
9.1%
a 18
9.1%
r 18
9.1%
t 9
 
4.5%
o 9
 
4.5%
m 9
 
4.5%
s 9
 
4.5%
Other values (3) 27
13.6%
Uppercase Letter
ValueCountFrequency (%)
T 9
50.0%
F 9
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216
88.9%
Common 27
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 27
12.5%
e 27
12.5%
i 27
12.5%
g 18
 
8.3%
a 18
 
8.3%
r 18
 
8.3%
t 9
 
4.2%
o 9
 
4.2%
T 9
 
4.2%
F 9
 
4.2%
Other values (5) 45
20.8%
Common
ValueCountFrequency (%)
18
66.7%
- 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 27
11.1%
e 27
11.1%
i 27
11.1%
g 18
 
7.4%
18
 
7.4%
a 18
 
7.4%
r 18
 
7.4%
t 9
 
3.7%
o 9
 
3.7%
T 9
 
3.7%
Other values (7) 63
25.9%

1960
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
2007.69

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2007.69
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
2007.69 1
 
11.1%

Length

2022-12-28T11:59:45.207089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.788766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
2007.69 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
0 2
 
13.3%
2 1
 
6.7%
7 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
9 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
33.3%
2 1
16.7%
7 1
16.7%
6 1
16.7%
9 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
0 2
 
13.3%
2 1
 
6.7%
7 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
9 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
0 2
 
13.3%
2 1
 
6.7%
7 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
9 1
 
6.7%

1961
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
2131.08

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2131.08
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
2131.08 1
 
11.1%

Length

2022-12-28T11:59:45.819528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.854936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
2131.08 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
1 2
 
13.3%
2 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
8 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
33.3%
2 1
16.7%
3 1
16.7%
0 1
16.7%
8 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
1 2
 
13.3%
2 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
8 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
1 2
 
13.3%
2 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
8 1
 
6.7%

1962
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
2312.56

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2312.56
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
2312.56 1
 
11.1%

Length

2022-12-28T11:59:45.885438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.920397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
2312.56 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
3 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
33.3%
3 1
16.7%
1 1
16.7%
5 1
16.7%
6 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
3 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
3 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%
6 1
 
6.7%

1963
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
2493.62

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2493.62
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
2493.62 1
 
11.1%

Length

2022-12-28T11:59:45.950716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:45.985519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
2493.62 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
4 1
 
6.7%
9 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
33.3%
4 1
16.7%
9 1
16.7%
3 1
16.7%
6 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
4 1
 
6.7%
9 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
2 2
 
13.3%
4 1
 
6.7%
9 1
 
6.7%
3 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%

1964
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
2757.68

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2757.68
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
2757.68 1
 
11.1%

Length

2022-12-28T11:59:46.015804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.050648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
2757.68 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
7 2
 
13.3%
2 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 2
33.3%
2 1
16.7%
5 1
16.7%
6 1
16.7%
8 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
7 2
 
13.3%
2 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
7 2
 
13.3%
2 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

1965
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
3045.28

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row3045.28
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
3045.28 1
 
11.1%

Length

2022-12-28T11:59:46.080955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.115831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
3045.28 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
3 1
 
6.7%
0 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1
16.7%
0 1
16.7%
4 1
16.7%
5 1
16.7%
2 1
16.7%
8 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
3 1
 
6.7%
0 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
3 1
 
6.7%
0 1
 
6.7%
4 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
2 1
 
6.7%
8 1
 
6.7%

1966
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
3554.01

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row3554.01
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
3554.01 1
 
11.1%

Length

2022-12-28T11:59:46.146203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.181059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
3554.01 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
5 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
1 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2
33.3%
3 1
16.7%
4 1
16.7%
0 1
16.7%
1 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
5 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
5 2
 
13.3%
3 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
1 1
 
6.7%

1967
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
4018.48

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row4018.48
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
4018.48 1
 
11.1%

Length

2022-12-28T11:59:46.211389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.246086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
4018.48 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
0 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2
33.3%
8 2
33.3%
0 1
16.7%
1 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
0 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
0 1
 
6.7%
1 1
 
6.7%
. 1
 
6.7%

1968
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
4395.06

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row4395.06
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
4395.06 1
 
11.1%

Length

2022-12-28T11:59:46.276415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.311067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
4395.06 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
4 1
 
6.7%
3 1
 
6.7%
9 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
6 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1
16.7%
3 1
16.7%
9 1
16.7%
5 1
16.7%
0 1
16.7%
6 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
4 1
 
6.7%
3 1
 
6.7%
9 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
6 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
4 1
 
6.7%
3 1
 
6.7%
9 1
 
6.7%
5 1
 
6.7%
. 1
 
6.7%
0 1
 
6.7%
6 1
 
6.7%

1969
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
4874.85

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row4874.85
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
4874.85 1
 
11.1%

Length

2022-12-28T11:59:46.341196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.376486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
4874.85 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
7 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2
33.3%
8 2
33.3%
7 1
16.7%
5 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
7 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
4 2
 
13.3%
8 2
 
13.3%
7 1
 
6.7%
. 1
 
6.7%
5 1
 
6.7%

1970
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
5482.97

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row5482.97
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
5482.97 1
 
11.1%

Length

2022-12-28T11:59:46.406801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.441630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
5482.97 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
5 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
2 1
 
6.7%
. 1
 
6.7%
9 1
 
6.7%
7 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1
16.7%
4 1
16.7%
8 1
16.7%
2 1
16.7%
9 1
16.7%
7 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
5 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
2 1
 
6.7%
. 1
 
6.7%
9 1
 
6.7%
7 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
5 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
2 1
 
6.7%
. 1
 
6.7%
9 1
 
6.7%
7 1
 
6.7%

1971
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
6489.12

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row6489.12
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
6489.12 1
 
11.1%

Length

2022-12-28T11:59:46.471886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.507181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
6489.12 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
6 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
2 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1
16.7%
4 1
16.7%
8 1
16.7%
9 1
16.7%
1 1
16.7%
2 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
6 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
6 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
9 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
2 1
 
6.7%

1972
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
7290.14

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row7290.14
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
7290.14 1
 
11.1%

Length

2022-12-28T11:59:46.537509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.572332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
7290.14 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
7 1
 
6.7%
2 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
4 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1
16.7%
2 1
16.7%
9 1
16.7%
0 1
16.7%
1 1
16.7%
4 1
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
7 1
 
6.7%
2 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
4 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
7 1
 
6.7%
2 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%
4 1
 
6.7%

1973
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
8226.12

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row8226.12
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
8226.12 1
 
11.1%

Length

2022-12-28T11:59:46.602734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.637600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
8226.12 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
2 3
 
20.0%
8 1
 
6.7%
6 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3
50.0%
8 1
 
16.7%
6 1
 
16.7%
1 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
2 3
 
20.0%
8 1
 
6.7%
6 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
2 3
 
20.0%
8 1
 
6.7%
6 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

1974
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
9488.18

Length

Max length7
Median length1
Mean length1.6666667
Min length1

Characters and Unicode

Total characters15
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row9488.18
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
9488.18 1
 
11.1%

Length

2022-12-28T11:59:46.668034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.703091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
9488.18 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
53.3%
8 3
 
20.0%
9 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
60.0%
Decimal Number 6
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3
50.0%
9 1
 
16.7%
4 1
 
16.7%
1 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
53.3%
8 3
 
20.0%
9 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
53.3%
8 3
 
20.0%
9 1
 
6.7%
4 1
 
6.7%
. 1
 
6.7%
1 1
 
6.7%

1975
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
10726.42

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row10726.42
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
10726.42 1
 
11.1%

Length

2022-12-28T11:59:46.734059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.769702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
10726.42 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
28.6%
1 1
14.3%
0 1
14.3%
7 1
14.3%
6 1
14.3%
4 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

1976
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
11240.92

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row11240.92
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
11240.92 1
 
11.1%

Length

2022-12-28T11:59:46.800608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.836011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
11240.92 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
4 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
2 2
28.6%
4 1
14.3%
0 1
14.3%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
4 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
4 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

1977
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
11558.14

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row11558.14
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
11558.14 1
 
11.1%

Length

2022-12-28T11:59:46.867286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.902912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
11558.14 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 3
 
18.8%
5 2
 
12.5%
8 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
42.9%
5 2
28.6%
8 1
 
14.3%
4 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 3
 
18.8%
5 2
 
12.5%
8 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 3
 
18.8%
5 2
 
12.5%
8 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%

1978
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
12039.63

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row12039.63
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
12039.63 1
 
11.1%

Length

2022-12-28T11:59:46.933925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:46.969435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
12039.63 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
1 1
 
6.2%
2 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2
28.6%
1 1
14.3%
2 1
14.3%
0 1
14.3%
9 1
14.3%
6 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
1 1
 
6.2%
2 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
1 1
 
6.2%
2 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

1979
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
12801.23

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row12801.23
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
12801.23 1
 
11.1%

Length

2022-12-28T11:59:47.000449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.035970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
12801.23 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
2 2
28.6%
8 1
14.3%
0 1
14.3%
3 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
2 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

1980
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
13752.87

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row13752.87
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
13752.87 1
 
11.1%

Length

2022-12-28T11:59:47.066794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.102221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
13752.87 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
7 2
 
12.5%
1 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
8 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 2
28.6%
1 1
14.3%
3 1
14.3%
5 1
14.3%
2 1
14.3%
8 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
7 2
 
12.5%
1 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
8 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
7 2
 
12.5%
1 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
8 1
 
6.2%

1981
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
14891.46

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row14891.46
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
14891.46 1
 
11.1%

Length

2022-12-28T11:59:47.133162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.168685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
14891.46 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
4 2
 
12.5%
8 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
4 2
28.6%
8 1
14.3%
9 1
14.3%
6 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
4 2
 
12.5%
8 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
4 2
 
12.5%
8 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

1982
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
16098.38

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row16098.38
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
16098.38 1
 
11.1%

Length

2022-12-28T11:59:47.199539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.235321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
16098.38 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
8 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 2
28.6%
1 1
14.3%
6 1
14.3%
0 1
14.3%
9 1
14.3%
3 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
8 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
8 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
0 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
3 1
 
6.2%

1983
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
17452.64

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row17452.64
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
17452.64 1
 
11.1%

Length

2022-12-28T11:59:47.266262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.301808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
17452.64 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
4 2
 
12.5%
1 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2
28.6%
1 1
14.3%
7 1
14.3%
5 1
14.3%
2 1
14.3%
6 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
4 2
 
12.5%
1 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
4 2
 
12.5%
1 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
2 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%

1984
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
18061.29

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row18061.29
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
18061.29 1
 
11.1%

Length

2022-12-28T11:59:47.332652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.368702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
18061.29 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
2 1
 
6.2%
9 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
8 1
14.3%
0 1
14.3%
6 1
14.3%
2 1
14.3%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
2 1
 
6.2%
9 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
8 1
 
6.2%
0 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
2 1
 
6.2%
9 1
 
6.2%

1985
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
19035.67

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row19035.67
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
19035.67 1
 
11.1%

Length

2022-12-28T11:59:47.400699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.436249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
19035.67 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 1
 
6.2%
9 1
 
6.2%
0 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
14.3%
9 1
14.3%
0 1
14.3%
3 1
14.3%
5 1
14.3%
6 1
14.3%
7 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 1
 
6.2%
9 1
 
6.2%
0 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 1
 
6.2%
9 1
 
6.2%
0 1
 
6.2%
3 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%

1986
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
20409.07

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row20409.07
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
20409.07 1
 
11.1%

Length

2022-12-28T11:59:47.467232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.502775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
20409.07 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
0 3
 
18.8%
2 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
42.9%
2 1
 
14.3%
4 1
 
14.3%
9 1
 
14.3%
7 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
0 3
 
18.8%
2 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
0 3
 
18.8%
2 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

1987
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
21672.45

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row21672.45
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
21672.45 1
 
11.1%

Length

2022-12-28T11:59:47.533596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.569195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
21672.45 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
28.6%
1 1
14.3%
6 1
14.3%
7 1
14.3%
4 1
14.3%
5 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
1 1
 
6.2%
6 1
 
6.2%
7 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%

1988
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
23145.73

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row23145.73
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
23145.73 1
 
11.1%

Length

2022-12-28T11:59:47.600039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.635621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
23145.73 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2
28.6%
2 1
14.3%
1 1
14.3%
4 1
14.3%
5 1
14.3%
7 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
1 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

1989
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
25025.70

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row25025.70
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
25025.70 1
 
11.1%

Length

2022-12-28T11:59:47.666561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.702107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
25025.70 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
5 2
 
12.5%
0 2
 
12.5%
. 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
28.6%
5 2
28.6%
0 2
28.6%
7 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
5 2
 
12.5%
0 2
 
12.5%
. 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
2 2
 
12.5%
5 2
 
12.5%
0 2
 
12.5%
. 1
 
6.2%
7 1
 
6.2%

1990
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
26935.73

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row26935.73
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
26935.73 1
 
11.1%

Length

2022-12-28T11:59:47.733025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.768518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
26935.73 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
6 1
 
6.2%
9 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2
28.6%
2 1
14.3%
6 1
14.3%
9 1
14.3%
5 1
14.3%
7 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
6 1
 
6.2%
9 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 1
 
6.2%
6 1
 
6.2%
9 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
7 1
 
6.2%

1991
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
30376.49

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row30376.49
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
30376.49 1
 
11.1%

Length

2022-12-28T11:59:47.799750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.835235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
30376.49 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2
28.6%
0 1
14.3%
7 1
14.3%
6 1
14.3%
4 1
14.3%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
0 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
4 1
 
6.2%
9 1
 
6.2%

1992
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
32364.52

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row32364.52
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
32364.52 1
 
11.1%

Length

2022-12-28T11:59:47.866305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.902046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
32364.52 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 2
 
12.5%
6 1
 
6.2%
4 1
 
6.2%
. 1
 
6.2%
5 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2
28.6%
2 2
28.6%
6 1
14.3%
4 1
14.3%
5 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 2
 
12.5%
6 1
 
6.2%
4 1
 
6.2%
. 1
 
6.2%
5 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 2
 
12.5%
2 2
 
12.5%
6 1
 
6.2%
4 1
 
6.2%
. 1
 
6.2%
5 1
 
6.2%

1993
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
33475.30

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row33475.30
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
33475.30 1
 
11.1%

Length

2022-12-28T11:59:47.933641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:47.969229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
33475.30 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
3 3
 
18.8%
4 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
0 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3
42.9%
4 1
 
14.3%
7 1
 
14.3%
5 1
 
14.3%
0 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
3 3
 
18.8%
4 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
0 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
3 3
 
18.8%
4 1
 
6.2%
7 1
 
6.2%
5 1
 
6.2%
. 1
 
6.2%
0 1
 
6.2%

1994
Categorical

Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size200.0 B
*
34761.19

Length

Max length8
Median length1
Mean length1.7777778
Min length1

Characters and Unicode

Total characters16
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row34761.19
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 8
88.9%
34761.19 1
 
11.1%

Length

2022-12-28T11:59:48.000259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T11:59:48.035817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
8
88.9%
34761.19 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
3 1
 
6.2%
4 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 9
56.2%
Decimal Number 7
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
28.6%
3 1
14.3%
4 1
14.3%
7 1
14.3%
6 1
14.3%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
* 8
88.9%
. 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 16
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
3 1
 
6.2%
4 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8
50.0%
1 2
 
12.5%
3 1
 
6.2%
4 1
 
6.2%
7 1
 
6.2%
6 1
 
6.2%
. 1
 
6.2%
9 1
 
6.2%

1995
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8012.5233
Minimum1098.58
Maximum36056.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.064232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1098.58
5-th percentile1151.636
Q11515.91
median5961.21
Q38336.03
95-th percentile25530.774
Maximum36056.35
Range34957.77
Interquartile range (IQR)6820.12

Descriptive statistics

Standard deviation11021.245
Coefficient of variation (CV)1.3755024
Kurtosis6.7984587
Mean8012.5233
Median Absolute Deviation (MAD)4236.19
Skewness2.5069905
Sum72112.71
Variance1.2146784 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.092745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
36056.35 1
11.1%
9742.41 1
11.1%
8336.03 1
11.1%
1515.91 1
11.1%
6445.98 1
11.1%
1231.22 1
11.1%
5961.21 1
11.1%
1098.58 1
11.1%
1725.02 1
11.1%
ValueCountFrequency (%)
1098.58 1
11.1%
1231.22 1
11.1%
1515.91 1
11.1%
1725.02 1
11.1%
5961.21 1
11.1%
6445.98 1
11.1%
8336.03 1
11.1%
9742.41 1
11.1%
36056.35 1
11.1%
ValueCountFrequency (%)
36056.35 1
11.1%
9742.41 1
11.1%
8336.03 1
11.1%
6445.98 1
11.1%
5961.21 1
11.1%
1725.02 1
11.1%
1515.91 1
11.1%
1231.22 1
11.1%
1098.58 1
11.1%

1996
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8393.9289
Minimum1120.47
Maximum37772.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.123221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1120.47
5-th percentile1173.274
Q11586.16
median6253.8
Q38662.07
95-th percentile26711.852
Maximum37772.68
Range36652.21
Interquartile range (IQR)7075.91

Descriptive statistics

Standard deviation11539.634
Coefficient of variation (CV)1.3747596
Kurtosis6.8227163
Mean8393.9289
Median Absolute Deviation (MAD)4337.64
Skewness2.5114046
Sum75545.36
Variance1.3316316 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.151235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
37772.68 1
11.1%
10120.61 1
11.1%
8662.07 1
11.1%
1586.16 1
11.1%
6860.93 1
11.1%
1252.48 1
11.1%
6253.8 1
11.1%
1120.47 1
11.1%
1916.16 1
11.1%
ValueCountFrequency (%)
1120.47 1
11.1%
1252.48 1
11.1%
1586.16 1
11.1%
1916.16 1
11.1%
6253.8 1
11.1%
6860.93 1
11.1%
8662.07 1
11.1%
10120.61 1
11.1%
37772.68 1
11.1%
ValueCountFrequency (%)
37772.68 1
11.1%
10120.61 1
11.1%
8662.07 1
11.1%
6860.93 1
11.1%
6253.8 1
11.1%
1916.16 1
11.1%
1586.16 1
11.1%
1252.48 1
11.1%
1120.47 1
11.1%

1997
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8565.4133
Minimum1103.74
Maximum38544.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.182260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1103.74
5-th percentile1164.652
Q11551.96
median6559.91
Q38865.66
95-th percentile27193.1
Maximum38544.36
Range37440.62
Interquartile range (IQR)7313.7

Descriptive statistics

Standard deviation11778.377
Coefficient of variation (CV)1.375109
Kurtosis6.8140305
Mean8565.4133
Median Absolute Deviation (MAD)4629.19
Skewness2.5073916
Sum77088.72
Variance1.3873016 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.211903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
38544.36 1
11.1%
10166.21 1
11.1%
8865.66 1
11.1%
1551.96 1
11.1%
7110.14 1
11.1%
1256.02 1
11.1%
6559.91 1
11.1%
1103.74 1
11.1%
1930.72 1
11.1%
ValueCountFrequency (%)
1103.74 1
11.1%
1256.02 1
11.1%
1551.96 1
11.1%
1930.72 1
11.1%
6559.91 1
11.1%
7110.14 1
11.1%
8865.66 1
11.1%
10166.21 1
11.1%
38544.36 1
11.1%
ValueCountFrequency (%)
38544.36 1
11.1%
10166.21 1
11.1%
8865.66 1
11.1%
7110.14 1
11.1%
6559.91 1
11.1%
1930.72 1
11.1%
1551.96 1
11.1%
1256.02 1
11.1%
1103.74 1
11.1%

1998
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8906.0422
Minimum1148.6
Maximum40077.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.244204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1148.6
5-th percentile1235.708
Q11610.49
median6758.58
Q39409.03
95-th percentile28183.798
Maximum40077.19
Range38928.59
Interquartile range (IQR)7798.54

Descriptive statistics

Standard deviation12240.675
Coefficient of variation (CV)1.3744236
Kurtosis6.8356564
Mean8906.0422
Median Absolute Deviation (MAD)4759.49
Skewness2.5117394
Sum80154.38
Variance1.4983412 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.273844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
40077.19 1
11.1%
10343.71 1
11.1%
9409.03 1
11.1%
1610.49 1
11.1%
7441.32 1
11.1%
1366.37 1
11.1%
6758.58 1
11.1%
1148.6 1
11.1%
1999.09 1
11.1%
ValueCountFrequency (%)
1148.6 1
11.1%
1366.37 1
11.1%
1610.49 1
11.1%
1999.09 1
11.1%
6758.58 1
11.1%
7441.32 1
11.1%
9409.03 1
11.1%
10343.71 1
11.1%
40077.19 1
11.1%
ValueCountFrequency (%)
40077.19 1
11.1%
10343.71 1
11.1%
9409.03 1
11.1%
7441.32 1
11.1%
6758.58 1
11.1%
1999.09 1
11.1%
1610.49 1
11.1%
1366.37 1
11.1%
1148.6 1
11.1%

1999
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9184.49
Minimum1208.88
Maximum41330.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.305716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1208.88
5-th percentile1289.132
Q11633.22
median7097.79
Q39774.16
95-th percentile29042.194
Maximum41330.21
Range40121.33
Interquartile range (IQR)8140.94

Descriptive statistics

Standard deviation12625.678
Coefficient of variation (CV)1.3746738
Kurtosis6.8281747
Mean9184.49
Median Absolute Deviation (MAD)5094.12
Skewness2.5097339
Sum82660.41
Variance1.5940774 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.335419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
41330.21 1
11.1%
10610.17 1
11.1%
9774.16 1
11.1%
1633.22 1
11.1%
7592.8 1
11.1%
1409.51 1
11.1%
7097.79 1
11.1%
1208.88 1
11.1%
2003.67 1
11.1%
ValueCountFrequency (%)
1208.88 1
11.1%
1409.51 1
11.1%
1633.22 1
11.1%
2003.67 1
11.1%
7097.79 1
11.1%
7592.8 1
11.1%
9774.16 1
11.1%
10610.17 1
11.1%
41330.21 1
11.1%
ValueCountFrequency (%)
41330.21 1
11.1%
10610.17 1
11.1%
9774.16 1
11.1%
7592.8 1
11.1%
7097.79 1
11.1%
2003.67 1
11.1%
1633.22 1
11.1%
1409.51 1
11.1%
1208.88 1
11.1%

2000
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9571.66
Minimum1235.41
Maximum43072.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.367801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1235.41
5-th percentile1326.63
Q11821.73
median7453.19
Q310243.46
95-th percentile30157.914
Maximum43072.47
Range41837.06
Interquartile range (IQR)8421.73

Descriptive statistics

Standard deviation13148.766
Coefficient of variation (CV)1.3737185
Kurtosis6.8591467
Mean9571.66
Median Absolute Deviation (MAD)5413.14
Skewness2.5155282
Sum86144.94
Variance1.7289005 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.395020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
43072.47 1
11.1%
10786.08 1
11.1%
10243.46 1
11.1%
1821.73 1
11.1%
8029.09 1
11.1%
1463.46 1
11.1%
7453.19 1
11.1%
1235.41 1
11.1%
2040.05 1
11.1%
ValueCountFrequency (%)
1235.41 1
11.1%
1463.46 1
11.1%
1821.73 1
11.1%
2040.05 1
11.1%
7453.19 1
11.1%
8029.09 1
11.1%
10243.46 1
11.1%
10786.08 1
11.1%
43072.47 1
11.1%
ValueCountFrequency (%)
43072.47 1
11.1%
10786.08 1
11.1%
10243.46 1
11.1%
8029.09 1
11.1%
7453.19 1
11.1%
2040.05 1
11.1%
1821.73 1
11.1%
1463.46 1
11.1%
1235.41 1
11.1%

2001
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10167.552
Minimum1303.51
Maximum45753.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.423702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1303.51
5-th percentile1390.742
Q11932.02
median7908.11
Q310790.34
95-th percentile32099.758
Maximum45753.99
Range44450.48
Interquartile range (IQR)8858.32

Descriptive statistics

Standard deviation13977.405
Coefficient of variation (CV)1.374707
Kurtosis6.8283309
Mean10167.552
Median Absolute Deviation (MAD)5823.26
Skewness2.5086429
Sum91507.97
Variance1.9536786 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.450905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
45753.99 1
11.1%
11618.41 1
11.1%
10790.34 1
11.1%
1932.02 1
11.1%
8595.15 1
11.1%
1521.59 1
11.1%
7908.11 1
11.1%
1303.51 1
11.1%
2084.85 1
11.1%
ValueCountFrequency (%)
1303.51 1
11.1%
1521.59 1
11.1%
1932.02 1
11.1%
2084.85 1
11.1%
7908.11 1
11.1%
8595.15 1
11.1%
10790.34 1
11.1%
11618.41 1
11.1%
45753.99 1
11.1%
ValueCountFrequency (%)
45753.99 1
11.1%
11618.41 1
11.1%
10790.34 1
11.1%
8595.15 1
11.1%
7908.11 1
11.1%
2084.85 1
11.1%
1932.02 1
11.1%
1521.59 1
11.1%
1303.51 1
11.1%

2002
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10584.273
Minimum1339.68
Maximum47629.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.478285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1339.68
5-th percentile1429.664
Q12063.49
median8012.94
Q311139.31
95-th percentile33418.978
Maximum47629.23
Range46289.55
Interquartile range (IQR)9075.82

Descriptive statistics

Standard deviation14549.574
Coefficient of variation (CV)1.3746408
Kurtosis6.830387
Mean10584.273
Median Absolute Deviation (MAD)5837.62
Skewness2.5090709
Sum95258.46
Variance2.1169011 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.506306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
47629.23 1
11.1%
12103.6 1
11.1%
11139.31 1
11.1%
2063.49 1
11.1%
9230.25 1
11.1%
1564.64 1
11.1%
8012.94 1
11.1%
1339.68 1
11.1%
2175.32 1
11.1%
ValueCountFrequency (%)
1339.68 1
11.1%
1564.64 1
11.1%
2063.49 1
11.1%
2175.32 1
11.1%
8012.94 1
11.1%
9230.25 1
11.1%
11139.31 1
11.1%
12103.6 1
11.1%
47629.23 1
11.1%
ValueCountFrequency (%)
47629.23 1
11.1%
12103.6 1
11.1%
11139.31 1
11.1%
9230.25 1
11.1%
8012.94 1
11.1%
2175.32 1
11.1%
2063.49 1
11.1%
1564.64 1
11.1%
1339.68 1
11.1%

2003
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10984.114
Minimum1388.79
Maximum49428.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.533601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1388.79
5-th percentile1468.198
Q12172.11
median8526.11
Q311535.01
95-th percentile34638.768
Maximum49428.52
Range48039.73
Interquartile range (IQR)9362.9

Descriptive statistics

Standard deviation15097.624
Coefficient of variation (CV)1.3744962
Kurtosis6.8359608
Mean10984.114
Median Absolute Deviation (MAD)6314.64
Skewness2.5093877
Sum98857.03
Variance2.2793824 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.563518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
49428.52 1
11.1%
12454.14 1
11.1%
11535.01 1
11.1%
2172.11 1
11.1%
9553.57 1
11.1%
1587.31 1
11.1%
8526.11 1
11.1%
1388.79 1
11.1%
2211.47 1
11.1%
ValueCountFrequency (%)
1388.79 1
11.1%
1587.31 1
11.1%
2172.11 1
11.1%
2211.47 1
11.1%
8526.11 1
11.1%
9553.57 1
11.1%
11535.01 1
11.1%
12454.14 1
11.1%
49428.52 1
11.1%
ValueCountFrequency (%)
49428.52 1
11.1%
12454.14 1
11.1%
11535.01 1
11.1%
9553.57 1
11.1%
8526.11 1
11.1%
2211.47 1
11.1%
2172.11 1
11.1%
1587.31 1
11.1%
1388.79 1
11.1%

2004
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11413.446
Minimum1440.77
Maximum51360.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.591819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1440.77
5-th percentile1557.058
Q12193.93
median8821.5
Q311959.02
95-th percentile35987.162
Maximum51360.51
Range49919.74
Interquartile range (IQR)9765.09

Descriptive statistics

Standard deviation15686.019
Coefficient of variation (CV)1.3743456
Kurtosis6.8405536
Mean11413.446
Median Absolute Deviation (MAD)6524.74
Skewness2.5104572
Sum102721.01
Variance2.4605119 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.621705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
51360.51 1
11.1%
12927.14 1
11.1%
11959.02 1
11.1%
2193.93 1
11.1%
9989.89 1
11.1%
1731.49 1
11.1%
8821.5 1
11.1%
1440.77 1
11.1%
2296.76 1
11.1%
ValueCountFrequency (%)
1440.77 1
11.1%
1731.49 1
11.1%
2193.93 1
11.1%
2296.76 1
11.1%
8821.5 1
11.1%
9989.89 1
11.1%
11959.02 1
11.1%
12927.14 1
11.1%
51360.51 1
11.1%
ValueCountFrequency (%)
51360.51 1
11.1%
12927.14 1
11.1%
11959.02 1
11.1%
9989.89 1
11.1%
8821.5 1
11.1%
2296.76 1
11.1%
2193.93 1
11.1%
1731.49 1
11.1%
1440.77 1
11.1%

2005
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11641.809
Minimum1440.43
Maximum52388.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.649788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1440.43
5-th percentile1595.754
Q12217.5
median8936.27
Q312583.98
95-th percentile36512.58
Maximum52388.14
Range50947.71
Interquartile range (IQR)10366.48

Descriptive statistics

Standard deviation15996.568
Coefficient of variation (CV)1.3740621
Kurtosis6.849921
Mean11641.809
Median Absolute Deviation (MAD)6604.82
Skewness2.5119534
Sum104776.28
Variance2.5589019 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.679736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
52388.14 1
11.1%
12583.98 1
11.1%
12699.24 1
11.1%
2217.5 1
11.1%
10350.53 1
11.1%
1828.74 1
11.1%
8936.27 1
11.1%
1440.43 1
11.1%
2331.45 1
11.1%
ValueCountFrequency (%)
1440.43 1
11.1%
1828.74 1
11.1%
2217.5 1
11.1%
2331.45 1
11.1%
8936.27 1
11.1%
10350.53 1
11.1%
12583.98 1
11.1%
12699.24 1
11.1%
52388.14 1
11.1%
ValueCountFrequency (%)
52388.14 1
11.1%
12699.24 1
11.1%
12583.98 1
11.1%
10350.53 1
11.1%
8936.27 1
11.1%
2331.45 1
11.1%
2217.5 1
11.1%
1828.74 1
11.1%
1440.43 1
11.1%

2006
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11788.362
Minimum1493.83
Maximum53047.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.708396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1493.83
5-th percentile1622.374
Q12328.62
median8878.78
Q312584.84
95-th percentile37052.458
Maximum53047.63
Range51553.8
Interquartile range (IQR)10256.22

Descriptive statistics

Standard deviation16191.996
Coefficient of variation (CV)1.3735577
Kurtosis6.86482
Mean11788.362
Median Absolute Deviation (MAD)6456.35
Skewness2.5160673
Sum106095.26
Variance2.6218072 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.738312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
53047.63 1
11.1%
12584.84 1
11.1%
13059.7 1
11.1%
2328.62 1
11.1%
10464.24 1
11.1%
1815.19 1
11.1%
8878.78 1
11.1%
1493.83 1
11.1%
2422.43 1
11.1%
ValueCountFrequency (%)
1493.83 1
11.1%
1815.19 1
11.1%
2328.62 1
11.1%
2422.43 1
11.1%
8878.78 1
11.1%
10464.24 1
11.1%
12584.84 1
11.1%
13059.7 1
11.1%
53047.63 1
11.1%
ValueCountFrequency (%)
53047.63 1
11.1%
13059.7 1
11.1%
12584.84 1
11.1%
10464.24 1
11.1%
8878.78 1
11.1%
2422.43 1
11.1%
2328.62 1
11.1%
1815.19 1
11.1%
1493.83 1
11.1%

2007
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12327.507
Minimum1667.98
Maximum55473.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.766446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1667.98
5-th percentile1749.932
Q12455.55
median9131.14
Q313088.7
95-th percentile38756.964
Maximum55473.78
Range53805.8
Interquartile range (IQR)10633.15

Descriptive statistics

Standard deviation16927.874
Coefficient of variation (CV)1.3731791
Kurtosis6.8760851
Mean12327.507
Median Absolute Deviation (MAD)6611.57
Skewness2.5190205
Sum110947.56
Variance2.8655293 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.796419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55473.78 1
11.1%
13088.7 1
11.1%
13681.74 1
11.1%
2455.55 1
11.1%
11056.24 1
11.1%
1872.86 1
11.1%
9131.14 1
11.1%
1667.98 1
11.1%
2519.57 1
11.1%
ValueCountFrequency (%)
1667.98 1
11.1%
1872.86 1
11.1%
2455.55 1
11.1%
2519.57 1
11.1%
9131.14 1
11.1%
11056.24 1
11.1%
13088.7 1
11.1%
13681.74 1
11.1%
55473.78 1
11.1%
ValueCountFrequency (%)
55473.78 1
11.1%
13681.74 1
11.1%
13088.7 1
11.1%
11056.24 1
11.1%
9131.14 1
11.1%
2519.57 1
11.1%
2455.55 1
11.1%
1872.86 1
11.1%
1667.98 1
11.1%

2008
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13014.079
Minimum1749.01
Maximum58563.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.825292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1749.01
5-th percentile1852.898
Q12551.88
median9529.89
Q313957.53
95-th percentile40927.32
Maximum58563.36
Range56814.35
Interquartile range (IQR)11405.65

Descriptive statistics

Standard deviation17872.069
Coefficient of variation (CV)1.3732872
Kurtosis6.8719737
Mean13014.079
Median Absolute Deviation (MAD)6802.49
Skewness2.5187456
Sum117126.71
Variance3.1941084 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.852988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
58563.36 1
11.1%
13957.53 1
11.1%
14473.26 1
11.1%
2551.88 1
11.1%
11565.65 1
11.1%
2008.73 1
11.1%
9529.89 1
11.1%
1749.01 1
11.1%
2727.4 1
11.1%
ValueCountFrequency (%)
1749.01 1
11.1%
2008.73 1
11.1%
2551.88 1
11.1%
2727.4 1
11.1%
9529.89 1
11.1%
11565.65 1
11.1%
13957.53 1
11.1%
14473.26 1
11.1%
58563.36 1
11.1%
ValueCountFrequency (%)
58563.36 1
11.1%
14473.26 1
11.1%
13957.53 1
11.1%
11565.65 1
11.1%
9529.89 1
11.1%
2727.4 1
11.1%
2551.88 1
11.1%
2008.73 1
11.1%
1749.01 1
11.1%

2009
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13590.499
Minimum1907.81
Maximum61157.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.883070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1907.81
5-th percentile1968.134
Q12757.04
median10014.31
Q314456.92
95-th percentile42690.002
Maximum61157.25
Range59249.44
Interquartile range (IQR)11699.88

Descriptive statistics

Standard deviation18652.54
Coefficient of variation (CV)1.3724691
Kurtosis6.8980838
Mean13590.499
Median Absolute Deviation (MAD)7212.7
Skewness2.5241551
Sum122314.49
Variance3.4791724 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.910537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
61157.25 1
11.1%
14456.92 1
11.1%
14989.13 1
11.1%
2757.04 1
11.1%
12171.8 1
11.1%
2058.62 1
11.1%
10014.31 1
11.1%
1907.81 1
11.1%
2801.61 1
11.1%
ValueCountFrequency (%)
1907.81 1
11.1%
2058.62 1
11.1%
2757.04 1
11.1%
2801.61 1
11.1%
10014.31 1
11.1%
12171.8 1
11.1%
14456.92 1
11.1%
14989.13 1
11.1%
61157.25 1
11.1%
ValueCountFrequency (%)
61157.25 1
11.1%
14989.13 1
11.1%
14456.92 1
11.1%
12171.8 1
11.1%
10014.31 1
11.1%
2801.61 1
11.1%
2757.04 1
11.1%
2058.62 1
11.1%
1907.81 1
11.1%

2010
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13903.33
Minimum1706.72
Maximum62564.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.940325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1706.72
5-th percentile2129.444
Q12918.44
median10083.01
Q313373.44
95-th percentile43862.312
Maximum62564.98
Range60858.26
Interquartile range (IQR)10455

Descriptive statistics

Standard deviation19018.634
Coefficient of variation (CV)1.3679193
Kurtosis7.0411192
Mean13903.33
Median Absolute Deviation (MAD)6760.79
Skewness2.5574877
Sum125129.97
Variance3.6170844 × 108
MonotonicityNot monotonic
2022-12-28T11:59:48.967864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
62564.98 1
11.1%
13373.44 1
11.1%
15808.31 1
11.1%
2763.53 1
11.1%
12589.32 1
11.1%
3322.22 1
11.1%
10083.01 1
11.1%
1706.72 1
11.1%
2918.44 1
11.1%
ValueCountFrequency (%)
1706.72 1
11.1%
2763.53 1
11.1%
2918.44 1
11.1%
3322.22 1
11.1%
10083.01 1
11.1%
12589.32 1
11.1%
13373.44 1
11.1%
15808.31 1
11.1%
62564.98 1
11.1%
ValueCountFrequency (%)
62564.98 1
11.1%
15808.31 1
11.1%
13373.44 1
11.1%
12589.32 1
11.1%
10083.01 1
11.1%
3322.22 1
11.1%
2918.44 1
11.1%
2763.53 1
11.1%
1706.72 1
11.1%

2011
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14276.151
Minimum1695.75
Maximum64242.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:48.997658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1695.75
5-th percentile2132.858
Q12996.12
median10097.63
Q313582.55
95-th percentile44989.11
Maximum64242.69
Range62546.94
Interquartile range (IQR)10586.43

Descriptive statistics

Standard deviation19519.123
Coefficient of variation (CV)1.3672539
Kurtosis7.0624835
Mean14276.151
Median Absolute Deviation (MAD)6381.04
Skewness2.5622205
Sum128485.36
Variance3.8099616 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.025049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
64242.69 1
11.1%
13582.55 1
11.1%
16108.74 1
11.1%
2788.52 1
11.1%
13256.77 1
11.1%
3716.59 1
11.1%
10097.63 1
11.1%
1695.75 1
11.1%
2996.12 1
11.1%
ValueCountFrequency (%)
1695.75 1
11.1%
2788.52 1
11.1%
2996.12 1
11.1%
3716.59 1
11.1%
10097.63 1
11.1%
13256.77 1
11.1%
13582.55 1
11.1%
16108.74 1
11.1%
64242.69 1
11.1%
ValueCountFrequency (%)
64242.69 1
11.1%
16108.74 1
11.1%
13582.55 1
11.1%
13256.77 1
11.1%
10097.63 1
11.1%
3716.59 1
11.1%
2996.12 1
11.1%
2788.52 1
11.1%
1695.75 1
11.1%

2012
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14780.541
Minimum1699.66
Maximum66512.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.054871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1699.66
5-th percentile2153.276
Q12898.9
median10181.46
Q314176.31
95-th percentile46677.254
Maximum66512.43
Range64812.77
Interquartile range (IQR)11277.41

Descriptive statistics

Standard deviation20233.512
Coefficient of variation (CV)1.3689291
Kurtosis7.0083177
Mean14780.541
Median Absolute Deviation (MAD)6743.03
Skewness2.5507523
Sum133024.87
Variance4.0939502 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.082503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
66512.43 1
11.1%
14176.31 1
11.1%
16924.49 1
11.1%
2833.7 1
11.1%
13831.65 1
11.1%
3966.27 1
11.1%
10181.46 1
11.1%
1699.66 1
11.1%
2898.9 1
11.1%
ValueCountFrequency (%)
1699.66 1
11.1%
2833.7 1
11.1%
2898.9 1
11.1%
3966.27 1
11.1%
10181.46 1
11.1%
13831.65 1
11.1%
14176.31 1
11.1%
16924.49 1
11.1%
66512.43 1
11.1%
ValueCountFrequency (%)
66512.43 1
11.1%
16924.49 1
11.1%
14176.31 1
11.1%
13831.65 1
11.1%
10181.46 1
11.1%
3966.27 1
11.1%
2898.9 1
11.1%
2833.7 1
11.1%
1699.66 1
11.1%

2013
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15359.561
Minimum1780.63
Maximum69118.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.112178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1780.63
5-th percentile2206.402
Q12925.49
median10418.93
Q314791.18
95-th percentile48545.846
Maximum69118.03
Range67337.4
Interquartile range (IQR)11865.69

Descriptive statistics

Standard deviation21026.043
Coefficient of variation (CV)1.3689221
Kurtosis7.0082505
Mean15359.561
Median Absolute Deviation (MAD)7268.64
Skewness2.5511245
Sum138236.05
Variance4.4209447 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.140471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
69118.03 1
11.1%
14791.18 1
11.1%
17687.57 1
11.1%
2925.49 1
11.1%
14255.11 1
11.1%
4414.05 1
11.1%
10418.93 1
11.1%
1780.63 1
11.1%
2845.06 1
11.1%
ValueCountFrequency (%)
1780.63 1
11.1%
2845.06 1
11.1%
2925.49 1
11.1%
4414.05 1
11.1%
10418.93 1
11.1%
14255.11 1
11.1%
14791.18 1
11.1%
17687.57 1
11.1%
69118.03 1
11.1%
ValueCountFrequency (%)
69118.03 1
11.1%
17687.57 1
11.1%
14791.18 1
11.1%
14255.11 1
11.1%
10418.93 1
11.1%
4414.05 1
11.1%
2925.49 1
11.1%
2845.06 1
11.1%
1780.63 1
11.1%

2014
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15873.162
Minimum1852.44
Maximum71429.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.171221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1852.44
5-th percentile2259.488
Q13079.71
median10604.07
Q314947.37
95-th percentile50329.848
Maximum71429.22
Range69576.78
Interquartile range (IQR)11867.66

Descriptive statistics

Standard deviation21729.046
Coefficient of variation (CV)1.3689173
Kurtosis7.0071586
Mean15873.162
Median Absolute Deviation (MAD)7524.36
Skewness2.5522622
Sum142858.46
Variance4.7215143 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.199460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
71429.22 1
11.1%
14947.37 1
11.1%
18680.79 1
11.1%
3079.71 1
11.1%
14627.86 1
11.1%
4766.94 1
11.1%
10604.07 1
11.1%
1852.44 1
11.1%
2870.06 1
11.1%
ValueCountFrequency (%)
1852.44 1
11.1%
2870.06 1
11.1%
3079.71 1
11.1%
4766.94 1
11.1%
10604.07 1
11.1%
14627.86 1
11.1%
14947.37 1
11.1%
18680.79 1
11.1%
71429.22 1
11.1%
ValueCountFrequency (%)
71429.22 1
11.1%
18680.79 1
11.1%
14947.37 1
11.1%
14627.86 1
11.1%
10604.07 1
11.1%
4766.94 1
11.1%
3079.71 1
11.1%
2870.06 1
11.1%
1852.44 1
11.1%

2015
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16529.919
Minimum1877.56
Maximum74384.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.230355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1877.56
5-th percentile2300.584
Q13378.04
median11100.11
Q315385.88
95-th percentile52447.328
Maximum74384.64
Range72507.08
Interquartile range (IQR)12007.84

Descriptive statistics

Standard deviation22620.661
Coefficient of variation (CV)1.3684678
Kurtosis7.0214927
Mean16529.919
Median Absolute Deviation (MAD)7722.07
Skewness2.5556485
Sum148769.27
Variance5.116943 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.258436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
74384.64 1
11.1%
15385.88 1
11.1%
19541.36 1
11.1%
3378.04 1
11.1%
15129.31 1
11.1%
5037.25 1
11.1%
11100.11 1
11.1%
1877.56 1
11.1%
2935.12 1
11.1%
ValueCountFrequency (%)
1877.56 1
11.1%
2935.12 1
11.1%
3378.04 1
11.1%
5037.25 1
11.1%
11100.11 1
11.1%
15129.31 1
11.1%
15385.88 1
11.1%
19541.36 1
11.1%
74384.64 1
11.1%
ValueCountFrequency (%)
74384.64 1
11.1%
19541.36 1
11.1%
15385.88 1
11.1%
15129.31 1
11.1%
11100.11 1
11.1%
5037.25 1
11.1%
3378.04 1
11.1%
2935.12 1
11.1%
1877.56 1
11.1%

2016
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17212.268
Minimum1894.01
Maximum77455.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.290014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1894.01
5-th percentile2377.686
Q13560.31
median11702.09
Q315758
95-th percentile54647.678
Maximum77455.21
Range75561.2
Interquartile range (IQR)12197.69

Descriptive statistics

Standard deviation23535.171
Coefficient of variation (CV)1.3673486
Kurtosis7.0575693
Mean17212.268
Median Absolute Deviation (MAD)8141.78
Skewness2.5637826
Sum154910.41
Variance5.5390425 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.321885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
77455.21 1
11.1%
15758 1
11.1%
20436.38 1
11.1%
3560.31 1
11.1%
15448.66 1
11.1%
5552.55 1
11.1%
11702.09 1
11.1%
1894.01 1
11.1%
3103.2 1
11.1%
ValueCountFrequency (%)
1894.01 1
11.1%
3103.2 1
11.1%
3560.31 1
11.1%
5552.55 1
11.1%
11702.09 1
11.1%
15448.66 1
11.1%
15758 1
11.1%
20436.38 1
11.1%
77455.21 1
11.1%
ValueCountFrequency (%)
77455.21 1
11.1%
20436.38 1
11.1%
15758 1
11.1%
15448.66 1
11.1%
11702.09 1
11.1%
5552.55 1
11.1%
3560.31 1
11.1%
3103.2 1
11.1%
1894.01 1
11.1%

2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17698.448
Minimum1937.12
Maximum79643.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.354486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1937.12
5-th percentile2425.112
Q13662.72
median12088.35
Q315942.85
95-th percentile56229.074
Maximum79643.01
Range77705.89
Interquartile range (IQR)12280.13

Descriptive statistics

Standard deviation24187.529
Coefficient of variation (CV)1.3666469
Kurtosis7.0802317
Mean17698.448
Median Absolute Deviation (MAD)8425.63
Skewness2.5689883
Sum159286.03
Variance5.8503657 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.384402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
79643.01 1
11.1%
15718.28 1
11.1%
21108.17 1
11.1%
3662.72 1
11.1%
15942.85 1
11.1%
6028.43 1
11.1%
12088.35 1
11.1%
1937.12 1
11.1%
3157.1 1
11.1%
ValueCountFrequency (%)
1937.12 1
11.1%
3157.1 1
11.1%
3662.72 1
11.1%
6028.43 1
11.1%
12088.35 1
11.1%
15718.28 1
11.1%
15942.85 1
11.1%
21108.17 1
11.1%
79643.01 1
11.1%
ValueCountFrequency (%)
79643.01 1
11.1%
21108.17 1
11.1%
15942.85 1
11.1%
15718.28 1
11.1%
12088.35 1
11.1%
6028.43 1
11.1%
3662.72 1
11.1%
3157.1 1
11.1%
1937.12 1
11.1%

2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17831.512
Minimum2126.11
Maximum80241.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.416622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2126.11
5-th percentile2561.886
Q13822.54
median12213.71
Q316374.29
95-th percentile56446.472
Maximum80241.8
Range78115.69
Interquartile range (IQR)12551.75

Descriptive statistics

Standard deviation24327.403
Coefficient of variation (CV)1.3642928
Kurtosis7.1566731
Mean17831.512
Median Absolute Deviation (MAD)8391.17
Skewness2.5852645
Sum160483.61
Variance5.9182254 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.446370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
80241.8 1
11.1%
15547.74 1
11.1%
20753.48 1
11.1%
3822.54 1
11.1%
16374.29 1
11.1%
6188.39 1
11.1%
12213.71 1
11.1%
2126.11 1
11.1%
3215.55 1
11.1%
ValueCountFrequency (%)
2126.11 1
11.1%
3215.55 1
11.1%
3822.54 1
11.1%
6188.39 1
11.1%
12213.71 1
11.1%
15547.74 1
11.1%
16374.29 1
11.1%
20753.48 1
11.1%
80241.8 1
11.1%
ValueCountFrequency (%)
80241.8 1
11.1%
20753.48 1
11.1%
16374.29 1
11.1%
15547.74 1
11.1%
12213.71 1
11.1%
6188.39 1
11.1%
3822.54 1
11.1%
3215.55 1
11.1%
2126.11 1
11.1%

2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18327.078
Minimum1829.02
Maximum82471.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.478505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1829.02
5-th percentile2427.808
Q13886.68
median12602.42
Q316769.35
95-th percentile58144.096
Maximum82471.86
Range80642.84
Interquartile range (IQR)12882.67

Descriptive statistics

Standard deviation25022.618
Coefficient of variation (CV)1.3653359
Kurtosis7.1238576
Mean18327.078
Median Absolute Deviation (MAD)8715.74
Skewness2.577754
Sum164943.7
Variance6.2613141 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.508158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
82471.86 1
11.1%
15730.23 1
11.1%
21652.45 1
11.1%
3886.68 1
11.1%
16769.35 1
11.1%
6675.7 1
11.1%
12602.42 1
11.1%
1829.02 1
11.1%
3325.99 1
11.1%
ValueCountFrequency (%)
1829.02 1
11.1%
3325.99 1
11.1%
3886.68 1
11.1%
6675.7 1
11.1%
12602.42 1
11.1%
15730.23 1
11.1%
16769.35 1
11.1%
21652.45 1
11.1%
82471.86 1
11.1%
ValueCountFrequency (%)
82471.86 1
11.1%
21652.45 1
11.1%
16769.35 1
11.1%
15730.23 1
11.1%
12602.42 1
11.1%
6675.7 1
11.1%
3886.68 1
11.1%
3325.99 1
11.1%
1829.02 1
11.1%

2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18513.501
Minimum3017.5
Maximum83310.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.0 B
2022-12-28T11:59:49.540189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3017.5
5-th percentile3189.348
Q13769.68
median12693.54
Q317209.26
95-th percentile58057.56
Maximum83310.76
Range80293.26
Interquartile range (IQR)13439.58

Descriptive statistics

Standard deviation25161.034
Coefficient of variation (CV)1.3590641
Kurtosis7.3285308
Mean18513.501
Median Absolute Deviation (MAD)7484.22
Skewness2.6221774
Sum166621.51
Variance6.3307763 × 108
MonotonicityNot monotonic
2022-12-28T11:59:49.569938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
83310.76 1
11.1%
16223.16 1
11.1%
20177.76 1
11.1%
3769.68 1
11.1%
17209.26 1
11.1%
6772.73 1
11.1%
12693.54 1
11.1%
3017.5 1
11.1%
3447.12 1
11.1%
ValueCountFrequency (%)
3017.5 1
11.1%
3447.12 1
11.1%
3769.68 1
11.1%
6772.73 1
11.1%
12693.54 1
11.1%
16223.16 1
11.1%
17209.26 1
11.1%
20177.76 1
11.1%
83310.76 1
11.1%
ValueCountFrequency (%)
83310.76 1
11.1%
20177.76 1
11.1%
17209.26 1
11.1%
16223.16 1
11.1%
12693.54 1
11.1%
6772.73 1
11.1%
3769.68 1
11.1%
3447.12 1
11.1%
3017.5 1
11.1%

Interactions

2022-12-28T11:59:43.698313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:21.351351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.348179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.548825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.415965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.287855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.149617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.250334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.025706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.800946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.598338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.397146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.192252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.363388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.158419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.982540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.801033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.617454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.434676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.252250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.545323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.386641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.229652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.096143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.961471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.828059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.732019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:21.447295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.382290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.582532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.449755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.321526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.180230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.280924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.056075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.832003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.629654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.428081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.223663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.394781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.190991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.014717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.833351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.649510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.466734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.285198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.580025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.419810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.263502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.130157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.995314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.862423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.766108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:21.515722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.415884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.616502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.483830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.355727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.211044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.311385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.086835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.863822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.661056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.459329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.254872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.425778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.223818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.046756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.865928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.681269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.498834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.318173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.613072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.452870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.297454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.164339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.029276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.896521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.801586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:21.576161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.450603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.651369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.518866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.390773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.242795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.343108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-12-28T11:59:36.988502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.274751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.114506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.957887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.817215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.683733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.549934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.420451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.321372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.108073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.309799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.170866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.040232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.906706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.708979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.805241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.580977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.372599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.168162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.967509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.137333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.933013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.750667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.569419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.386908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.203330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.020757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.307847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.148882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.991691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.851439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.717530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.583849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.454253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.355895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.141489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.343163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.204941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.076848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.940545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.739460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.836017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.611496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.404312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.199280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.998946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.168665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.964633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.782930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.601913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.418912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.235362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.052958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.340950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.181873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.024738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.885690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.751440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.617854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.488235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.390030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.175017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.376834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.239073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.111527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.974455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.770176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.867168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.642181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.435512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.230783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.030061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.200145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.995687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.815251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.633676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.451076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.267383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.084999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.374595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.214679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.057815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.919718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.785283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.652194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.522518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.425062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.209471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.411023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.274456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.146831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.009669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.802489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.898918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.674031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.468148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.263530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.062460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.232966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.028137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.848868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.666833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.484241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.300442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.118655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.408612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.249579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.092016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.954731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.820389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.687332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.557650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.459996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.244083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.445570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.309720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.181984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.044529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.834249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.930722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.705501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.500823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.296521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.094959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.265335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.060931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.882348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.701235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.517479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.333915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.151943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.442768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.283660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.126660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.989689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.855951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.722600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.592895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.495327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.278836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.479822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.345451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.217508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.079555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.184973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.962428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.737499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.533348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.329754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.127457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.297685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.093460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.915459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.734661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.550657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.368086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.184939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.477080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.317684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.161572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.024811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.891089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.757528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.627816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:44.530508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:22.313171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:23.514431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:24.380791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:25.252815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:26.114719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.218219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:27.994017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:28.769285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:29.565931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:30.364278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:31.159831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:32.330188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.125967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:33.948864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:34.767859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:35.583886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:36.401279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:37.218788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:38.511142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:39.352628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:40.195731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.060981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:41.926309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:42.792755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T11:59:43.663090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-28T11:59:49.637235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
19951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020Leistung19601961196219631964196519661967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994
19951.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
19961.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
19971.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
19981.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
19991.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20011.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20021.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20031.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20041.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9830.9830.9830.9830.9830.9330.9330.9330.9170.9170.9170.9170.8830.8830.8830.8831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20050.9830.9830.9830.9830.9830.9830.9830.9830.9830.9831.0001.0001.0001.0001.0000.9500.9500.9500.9330.9330.9330.9330.9170.9170.9170.9171.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20060.9830.9830.9830.9830.9830.9830.9830.9830.9830.9831.0001.0001.0001.0001.0000.9500.9500.9500.9330.9330.9330.9330.9170.9170.9170.9171.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20070.9830.9830.9830.9830.9830.9830.9830.9830.9830.9831.0001.0001.0001.0001.0000.9500.9500.9500.9330.9330.9330.9330.9170.9170.9170.9171.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20080.9830.9830.9830.9830.9830.9830.9830.9830.9830.9831.0001.0001.0001.0001.0000.9500.9500.9500.9330.9330.9330.9330.9170.9170.9170.9171.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20090.9830.9830.9830.9830.9830.9830.9830.9830.9830.9831.0001.0001.0001.0001.0000.9500.9500.9500.9330.9330.9330.9330.9170.9170.9170.9171.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20100.9330.9330.9330.9330.9330.9330.9330.9330.9330.9330.9500.9500.9500.9500.9501.0001.0001.0000.9830.9830.9830.9830.9670.9670.9670.9671.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20110.9330.9330.9330.9330.9330.9330.9330.9330.9330.9330.9500.9500.9500.9500.9501.0001.0001.0000.9830.9830.9830.9830.9670.9670.9670.9671.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20120.9330.9330.9330.9330.9330.9330.9330.9330.9330.9330.9500.9500.9500.9500.9501.0001.0001.0000.9830.9830.9830.9830.9670.9670.9670.9671.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20130.9170.9170.9170.9170.9170.9170.9170.9170.9170.9170.9330.9330.9330.9330.9330.9830.9830.9831.0001.0001.0001.0000.9830.9830.9830.9831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20140.9170.9170.9170.9170.9170.9170.9170.9170.9170.9170.9330.9330.9330.9330.9330.9830.9830.9831.0001.0001.0001.0000.9830.9830.9830.9831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20150.9170.9170.9170.9170.9170.9170.9170.9170.9170.9170.9330.9330.9330.9330.9330.9830.9830.9831.0001.0001.0001.0000.9830.9830.9830.9831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20160.9170.9170.9170.9170.9170.9170.9170.9170.9170.9170.9330.9330.9330.9330.9330.9830.9830.9831.0001.0001.0001.0000.9830.9830.9830.9831.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20170.8830.8830.8830.8830.8830.8830.8830.8830.8830.8830.9170.9170.9170.9170.9170.9670.9670.9670.9830.9830.9830.9831.0001.0001.0001.0001.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20180.8830.8830.8830.8830.8830.8830.8830.8830.8830.8830.9170.9170.9170.9170.9170.9670.9670.9670.9830.9830.9830.9831.0001.0001.0001.0001.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20190.8830.8830.8830.8830.8830.8830.8830.8830.8830.8830.9170.9170.9170.9170.9170.9670.9670.9670.9830.9830.9830.9831.0001.0001.0001.0001.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
20200.8830.8830.8830.8830.8830.8830.8830.8830.8830.8830.9170.9170.9170.9170.9170.9670.9670.9670.9830.9830.9830.9831.0001.0001.0001.0001.0000.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.845
Leistung1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
19600.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0001.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19610.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19620.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19630.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19640.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19650.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19660.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19670.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19680.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19690.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19700.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19710.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19720.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19730.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19740.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19750.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19760.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19770.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19780.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19790.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19800.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19810.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19820.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19830.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19840.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.275
19850.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.2750.275
19860.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.2750.275
19870.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.2750.275
19880.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.2750.275
19890.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.2750.275
19900.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.2750.275
19910.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.2750.275
19920.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.2750.275
19930.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.0000.275
19940.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8450.8451.0000.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2750.2751.000

Missing values

2022-12-28T11:59:44.630751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-28T11:59:44.795603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LeistungserbringerLeistungFinanzierungsregime1960196119621963196419651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020
0Leistungserbringer - TotalLeistung - TotalFinanzierungsregime - Total2007.692131.082312.562493.622757.683045.283554.014018.484395.064874.855482.976489.127290.148226.129488.1810726.4211240.9211558.1412039.6312801.2313752.8714891.4616098.3817452.6418061.2919035.6720409.0721672.4523145.7325025.7026935.7330376.4932364.5233475.3034761.1936056.3537772.6838544.3640077.1941330.2143072.4745753.9947629.2349428.5251360.5152388.1453047.6355473.7858563.3661157.2562564.9864242.6966512.4369118.0371429.2274384.6477455.2179643.0180241.8082471.8683310.76
1Leistungserbringer - Total>> L Stationäre KurativbehandlungFinanzierungsregime - Total***********************************9742.4110120.6110166.2110343.7110610.1710786.0811618.4112103.6012454.1412927.1412583.9812584.8413088.7013957.5314456.9213373.4413582.5514176.3114791.1814947.3715385.8815758.0015718.2815547.7415730.2316223.16
2Leistungserbringer - Total>> M Ambulante KurativbehandlungFinanzierungsregime - Total***********************************8336.038662.078865.669409.039774.1610243.4610790.3411139.3111535.0111959.0212699.2413059.7013681.7414473.2614989.1315808.3116108.7416924.4917687.5718680.7919541.3620436.3821108.1720753.4821652.4520177.76
3Leistungserbringer - Total>> N RehabilitationFinanzierungsregime - Total***********************************1515.911586.161551.961610.491633.221821.731932.022063.492172.112193.932217.502328.622455.552551.882757.042763.532788.522833.702925.493079.713378.043560.313662.723822.543886.683769.68
4Leistungserbringer - Total>> O LangzeitpflegeFinanzierungsregime - Total***********************************6445.986860.937110.147441.327592.808029.098595.159230.259553.579989.8910350.5310464.2411056.2411565.6512171.8012589.3213256.7713831.6514255.1114627.8615129.3115448.6615942.8516374.2916769.3517209.26
5Leistungserbringer - Total>> P Unterstützende DienstleistungenFinanzierungsregime - Total***********************************1231.221252.481256.021366.371409.511463.461521.591564.641587.311731.491828.741815.191872.862008.732058.623322.223716.593966.274414.054766.945037.255552.556028.436188.396675.706772.73
6Leistungserbringer - Total>> Q GesundheitsgüterFinanzierungsregime - Total***********************************5961.216253.806559.916758.587097.797453.197908.118012.948526.118821.508936.278878.789131.149529.8910014.3110083.0110097.6310181.4610418.9310604.0711100.1111702.0912088.3512213.7112602.4212693.54
7Leistungserbringer - Total>> R PräventionFinanzierungsregime - Total***********************************1098.581120.471103.741148.601208.881235.411303.511339.681388.791440.771440.431493.831667.981749.011907.811706.721695.751699.661780.631852.441877.561894.011937.122126.111829.023017.50
8Leistungserbringer - Total>> S VerwaltungFinanzierungsregime - Total***********************************1725.021916.161930.721999.092003.672040.052084.852175.322211.472296.762331.452422.432519.572727.402801.612918.442996.122898.902845.062870.062935.123103.203157.103215.553325.993447.12
LeistungserbringerLeistungFinanzierungsregime1960196119621963196419651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020
0Leistungserbringer - TotalLeistung - TotalFinanzierungsregime - Total2007.692131.082312.562493.622757.683045.283554.014018.484395.064874.855482.976489.127290.148226.129488.1810726.4211240.9211558.1412039.6312801.2313752.8714891.4616098.3817452.6418061.2919035.6720409.0721672.4523145.7325025.7026935.7330376.4932364.5233475.3034761.1936056.3537772.6838544.3640077.1941330.2143072.4745753.9947629.2349428.5251360.5152388.1453047.6355473.7858563.3661157.2562564.9864242.6966512.4369118.0371429.2274384.6477455.2179643.0180241.8082471.8683310.76
1Leistungserbringer - Total>> L Stationäre KurativbehandlungFinanzierungsregime - Total***********************************9742.4110120.6110166.2110343.7110610.1710786.0811618.4112103.6012454.1412927.1412583.9812584.8413088.7013957.5314456.9213373.4413582.5514176.3114791.1814947.3715385.8815758.0015718.2815547.7415730.2316223.16
2Leistungserbringer - Total>> M Ambulante KurativbehandlungFinanzierungsregime - Total***********************************8336.038662.078865.669409.039774.1610243.4610790.3411139.3111535.0111959.0212699.2413059.7013681.7414473.2614989.1315808.3116108.7416924.4917687.5718680.7919541.3620436.3821108.1720753.4821652.4520177.76
3Leistungserbringer - Total>> N RehabilitationFinanzierungsregime - Total***********************************1515.911586.161551.961610.491633.221821.731932.022063.492172.112193.932217.502328.622455.552551.882757.042763.532788.522833.702925.493079.713378.043560.313662.723822.543886.683769.68
4Leistungserbringer - Total>> O LangzeitpflegeFinanzierungsregime - Total***********************************6445.986860.937110.147441.327592.808029.098595.159230.259553.579989.8910350.5310464.2411056.2411565.6512171.8012589.3213256.7713831.6514255.1114627.8615129.3115448.6615942.8516374.2916769.3517209.26
5Leistungserbringer - Total>> P Unterstützende DienstleistungenFinanzierungsregime - Total***********************************1231.221252.481256.021366.371409.511463.461521.591564.641587.311731.491828.741815.191872.862008.732058.623322.223716.593966.274414.054766.945037.255552.556028.436188.396675.706772.73
6Leistungserbringer - Total>> Q GesundheitsgüterFinanzierungsregime - Total***********************************5961.216253.806559.916758.587097.797453.197908.118012.948526.118821.508936.278878.789131.149529.8910014.3110083.0110097.6310181.4610418.9310604.0711100.1111702.0912088.3512213.7112602.4212693.54
7Leistungserbringer - Total>> R PräventionFinanzierungsregime - Total***********************************1098.581120.471103.741148.601208.881235.411303.511339.681388.791440.771440.431493.831667.981749.011907.811706.721695.751699.661780.631852.441877.561894.011937.122126.111829.023017.50
8Leistungserbringer - Total>> S VerwaltungFinanzierungsregime - Total***********************************1725.021916.161930.721999.092003.672040.052084.852175.322211.472296.762331.452422.432519.572727.402801.612918.442996.122898.902845.062870.062935.123103.203157.103215.553325.993447.12